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<table width="100%" summary="page for pension"><tr><td>pension</td><td align="right">R Documentation</td></tr></table>

<h2>Pension Funds Data</h2>

<h3>Description</h3>


<p>The total 1981 premium income of pension funds of Dutch firms,
for 18 Professional Branches, from de Wit (1982).
</p>


<h3>Usage</h3>

<pre>data(pension)</pre>


<h3>Format</h3>


<p>A data frame with 18 observations on the following 2 variables.
</p>

<dl>
<dt><code>Income</code></dt><dd><p>Premium Income (in millions of guilders)</p>
</dd>
<dt><code>Reserves</code></dt><dd><p>Premium Reserves (in millions of guilders)</p>
</dd>
</dl>



<h3>Source</h3>


<p>P. J. Rousseeuw and A. M. Leroy (1987)
<EM>Robust Regression and Outlier Detection</EM>;
Wiley, p.76, table 13.
</p>


<h3>Examples</h3>

<pre>
data(pension)
plot(pension)

summary(lm.p  &lt;-    lm(Reserves ~., data=pension))
summary(lmR.p &lt;- lmrob(Reserves ~., data=pension))
summary(lts.p &lt;- ltsReg(Reserves ~., data=pension))
abline( lm.p)
abline(lmR.p, col=2)
abline(lts.p, col=2, lty=2)

## MM: "the" solution is much simpler:
plot(pension, log = "xy")
lm.lp  &lt;-    lm(log(Reserves) ~ log(Income), data=pension)
lmR.lp &lt;- lmrob(log(Reserves) ~ log(Income), data=pension)
plot(log(Reserves) ~ log(Income), data=pension)
## no difference between LS and robust:
abline( lm.lp)
abline(lmR.lp, col=2)
</pre>


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